Transcript RZC-Chp6-Probabilityx (Slides)

Topic 6: Probability
Dr J Frost ([email protected])
www.drfrostmaths.com
Last modified: 31st August 2015
Slide guidance
Key to question types:
SMC
Senior Maths Challenge
Uni
Questions used in university
interviews (possibly Oxbridge).
www.ukmt.org.uk
The level, 1 being the easiest, 5
the hardest, will be indicated.
BMO
British Maths Olympiad
Those with high scores in the SMC
qualify for the BMO Round 1. The
top hundred students from this go
through to BMO Round 2.
Questions in these slides will have
their round indicated.
MAT
Maths Aptitude Test
Admissions test for those
applying for Maths and/or
Computer Science at Oxford
University.
University Interview
Frost
A Frosty Special
Questions from the deep dark
recesses of my head.
Classic
Classic
Well known problems in maths.
STEP
STEP Exam
Exam used as a condition for
offers to universities such as
Cambridge and Bath.
Slide guidance
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
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works particularly well with interactive whiteboards!).
Make sure you’re viewing the slides in slideshow mode.
For multiple choice questions (e.g. SMC), click your choice to
reveal the answer (try below!)
Question: The capital of Spain is:
A: London

B: Paris

C: Madrid

Note: Some of these slides will likely be unsuitable for
those who have not done S1/S2. For those that haven’t,
I recommend just covering Part 1 and Part 3d
(Geometric).
Topic 6: Probability
Part 1 – Manipulating Probabilities
Part 2 – Random Variables
a.
b.
c.
d.
e.
Random Variables
Discrete and Continuous Distributions
Mean and Expected Value
Uniform Distributions
Standard Deviation and Variance
Part 3 – Common Distributions
a.
b.
c.
d.
e.
Binomial
Bernoulli
Poisson
Geometric
Normal/Gaussian
Some starting notes
Only some of those reading this will have done a Statistics module at A Level. Therefore
only GCSE knowledge is assumed.
There is some overlap with the field of Combinatorics. For probability problems relating
to ‘arrangements’ of things, look there instead.
Probability and Stats questions in…
In university interviews…
In SMC
In STEP
Probability questions
frequently come up (although
not technically requiring any
more than GCSE theory).
In my experience, applicants
tend to do particularly bad at
these questions.
Harder probability questions are
quite rare (although one
appeared towards the end of
2012’s paper)
Two questions at
the end of every
paper. You could
avoid these, but
you broaden
your choice if
you prepare for
these.
In BMO
Used to be moderately common,
but less so nowadays
But some basic probability/statistics will broaden your maths ‘general knowledge’. You’ll
know for example what scientists at CERN mean when they refer in the news to the “5𝜎
test” needed to verify that a new particle has been discovered!
Topic 6 – Probability
Part 1: Manipulating Probabilities
Events and Sets
An event is a set of outcomes.
“Even number thrown on a die”
= {2, 4,? 6}
Given that events can be represented as sets, we can use set
operations. Suppose 𝐸 = {2, 4, 6}, say the event of throwing
an even number, and 𝑃 = {2, 3, 5}, say the event of throwing
a prime number. Then:
𝐸 ∩ 𝑃 = {2}?
? 5, 6}
𝐸 ∪ 𝑃 = {2, 3, 4,
∩ means set intersection. It gives the items
which are members of both sets. It represents
“numbers that are even AND prime”.
∪ means set union. It gives the items which
are members of either. It represents
“numbers that are even OR prime”.
GCSE Recap
When 𝐴 and 𝐵 are mutually exclusive (i.e. 𝐴 and 𝐵 can’t happen at the same
time, or more formally 𝐴 ∩ 𝐵 = ∅, where ∅ is the ‘empty set’)…
𝑃 𝐴 ∪ 𝐵 = 𝑃 𝐴 +?𝑃(𝐵)
When 𝐴 and 𝐵 are independent (i.e. 𝐴 and 𝐵 don’t influence each other)…
𝑃 𝐴 ∩ 𝐵 = 𝑃 𝐴 ×? 𝑃(𝐵)
When 𝐴 and 𝐵 are mutually exclusive…
𝑃 𝐴∩𝐵 =0
?
More useful identities
When 𝐴 and 𝐵 are not mutually exclusive…
𝑃 𝐴 ∪ 𝐵 = 𝑃 𝐴 + 𝑃 𝐵 ?− 𝑃(𝐴 ∩ 𝐵)
When 𝐴 and 𝐵 are independent and mutually exclusive…
𝑃 𝐴 ∪ 𝐵 = 𝑃 𝐴 + 𝑃 𝐵 ?− 𝑃 𝐴 𝑃(𝐵)
Conditional probabilities
We might want to express the probability of an event given that another
occurred:
The probability that A occurred given B
occurred.
𝑃 𝐴|𝐵
To appreciate conditional probabilities, consider a probability tree:
1st pick
2nd pick
3
6
4
7
Red
3
7
Green
3
6
Red
Green
This represents the
probability that a green
counter was picked
second GIVEN that a red
counter was picked first.
Conditional probabilities
Using the tree, we can construct the following identity for condition
probabilities:
𝑝(𝐵|𝐴)
𝑝(𝐴)
B
A
𝑃 𝐴 ∩ 𝐵 = 𝑃 𝐴 𝑃(𝐵|𝐴) or
𝑃 𝐴∩𝐵
𝑃 𝐵𝐴 =
𝑃 𝐴
𝑝(𝐴 ∩ 𝐵)
Conditional probabilities
If 𝐴 and 𝐵 are independent, then what is 𝑃(𝐴|𝐵)?
The Common Sense Method
If 𝐴 and 𝐵 are independent,
then the probability of 𝐴
occurring is not affected by
whether 𝐵 occurred, so:
𝑃 𝐴𝐵 =
? 𝑃(𝐴)
The Formal Method
𝑃 𝐴∩𝐵
𝑃 𝐴𝐵 =
𝑃 𝐵
=
𝑃 𝐴 𝑃 𝐵
𝑃 𝐵 ?
Because 𝐴 and 𝐵
are independent.
Examples
1
2
The events A and B are independent with 𝑃 𝐴 = 4 and 𝑃 𝐴 ∪ 𝐵 = 3 . Find:
(Source: Edexcel)
𝑃 𝐵
𝑃(𝐵′ |𝐴)
𝑃 𝐴∪𝐵 =𝑃 𝐴 +𝑃 𝐵 −𝑃 𝐴 𝑃 𝐵
2 1
1
= + 𝑃 𝐵 − 𝑃? 𝐵
3 4
4
5
𝑃 𝐵 =
9
𝑃 𝐵′ 𝐴 = 𝑃(𝐵′ ) because 𝐴 and 𝐵 are
independent.
𝑃 𝐴′ ∩ 𝐵
𝑃 𝐴′ ∩ 𝐵 = 𝑃 𝐴′ × 𝑃 𝐵
3
5
15
5
= 4 × 9 = 36 = 12
?
4
So 𝑃 𝐵′ 𝐴 = 9
?
Bayes’ Rule
Bayes’ Rule relates causes and effects. It allows us find the probability of the
cause given the effect, if we know the probability of the effect given the
cause.
𝑃 𝐸𝐶 𝑃 𝐶
𝑃 𝐶𝐸 =
𝑃 𝐸
Dr House is trying to find the cause of a disease. He
suspects Lupus (as he always does) due to their kidney
failure. The probability that someone has this symptom
if they did have Lupus is 0.2. The probability that a
random patient has kidney damage is 0.001, and the
probability they have Lupus 0.0001. What is the
probability they have Lupus given their observed
symptom?
0.2 × 0.0001
𝑃 𝐿𝐾 =
= 0.002
?
0.001
Bayes’ Rule
But we don’t always need to know the probability of the effect.
𝑷 𝑬𝑪 𝑷 𝑪
𝑷 𝑪𝑬 =
𝑷 𝑬
Notice that in the distribution 𝑃(𝐶|𝐸), 𝐸 is fixed, and the distribution is over
different causes, where 𝑐∈𝐶 𝑃 𝐶 𝐸 = 1. This suggests we can write:
𝑷 𝑪 𝑬 =𝒌𝑷 𝑬 𝑪 𝑷 𝑪
where 𝑘 is a normalising constant that is set to ensure our probabilities add
up to 1, i.e. 𝑃 𝐶 𝐸 + 𝑃 𝐶 ′ 𝐸 = 1
Question: The probability that a game is called off if it’s
raining is 0.7. The probability it’s called off if it didn’t rain
(e.g. due to player illness) is 0.05. The probability that it
rains on any given day is 0.2.
Andy Murray’s game is called off. What’s the probability
that rain was the cause?
Bayes’ Rule
Question: The probability that a game is called off if it’s
raining is 0.7. The probability it’s called off if it didn’t rain
(e.g. due to player illness) is 0.05. The probability that it
rains on any given day is 0.2.
Andy Murray’s game is called off. What’s the probability
that rain was the cause?
Write down information:
𝑃 𝐶 𝑅 = 0.7
𝑃 𝐶 𝑅′ = 0.05
?
So 𝑃 𝑅′ = 0.8
𝑃 𝑅 = 0.2
Then using Bayes’ Rule:
𝑃 𝑅 𝐶 = 𝑘 𝑃 𝐶 𝑅 𝑃 𝑅 = 0.14𝑘
𝑃 𝑅′ 𝐶 = 𝑘𝑃 𝐶 𝑅′ 𝑃 𝑅′ = 0.04𝑘
? = 1, so 𝑘 =
But 𝑃 𝑅 𝐶 + 𝑃 𝑅′ 𝐶 = 1. So 0.18𝑘
Then 𝑷 𝑹 𝑪 = 𝟎. 𝟏𝟒 ×
𝟓𝟎
𝟗
=
𝟕
𝟗
50
.
9
Topic 6 – Probability
Part 2: Random Variables
Random Variables
A random variable is a variable which can have multiple values, each with
an associated probability.
The variable can be thought of as a ‘trial’ or ‘experiment’, representing
something which can have a number of outcomes.
A random variable has 3 things associated with it:
The values the random variable can have
(e.g. outcomes of the throw of a die)
1
The outcomes
2
A probability function
The probability associated with each
outcome.
Parameters
These are constants used in our
probability function that can be set (e.g.
number of throws)
3
(formally known as the ‘support vector’)
Example random variables
This symbol means “for all”
Random variable (X)
Outcomes
Parameters?
The single throw
of a fair die.
{1, 2, 3, 4, 5, 6}
None
The single throw
of an unfair die.
{1, 2, 3, 4, 5, 6}
We can set the
probability of
?
each outcome:
p1, p2, …, p6
?
?
We use capital letters for
random variables.
Parameters are values we can control, but
do not change across different outcomes.
We’ll see plenty more examples.
?
Probability Function
1
𝑃 𝑋=𝑥 =
?6
𝑃 𝑋 = 𝑥𝑖 = 𝑝𝑖
∀𝑥
∀𝑖
?
For an outcome x and a random variable X, we
express the probability as 𝑃(𝑋 = 𝑥),
meaning “the probability that the random
variable X has the outcome x”.
We sometimes write 𝑝(𝑥) for short, with a
lowercase p.
In this example, we can use the probability
associated with the particular outcome. We
sometimes use 𝑥𝑖 to mean the ith outcome.
Sketching the probability function
It’s often helpful to show the probability
function as a graph. Suppose a random
variable X represents the single throw of a
biased die:
Probabilities
P(X)
1
2
3
4
X
5
6
Outcomes
Discrete vs Continuous Distributions
Discrete distributions are ones where the outcomes are discrete, e.g. throw of a die,
number of Heads seen in 10 throws, etc.
In contrast continuous distributions allow us to model things like height, weight, etc.
Here’s two possible probability functions:
Discrete
Continuous
0.3
P(X=h)
P(X=k)
0.4
0.2
0.1
1.2m 1.4m 1.6m 1.8m 2.0m 2.2m 2.4m
1
2
3
4
Number of times target hit (k)
Height of randomly picked person (h)
Discrete vs Continuous Distributions
Discrete
P(X=k)
0.4
Probabilities add up to 1.
i.e. 𝑥 𝑝 𝑥 = 1
0.3
All probabilities must be between 0
and 1, i.e.
0 ≤ 𝑝 𝑥 ≤ 1 ∀𝑥.
0.2
0.1
1
2
3
4
Number of times target hit (k)
We call the probability function the:
Probability mass function (PMF for
short)
Because our probability function is ultimately just a plain old function
(provided it meets the above properties), we usually see the function
written as “𝑓(𝑥)” rather than 𝑝(𝑥).
Continuous Distributions
𝟏.𝟔
𝟏.𝟒
i.e. We find the area under the
graph. Note that the area
under the entire graph will be
1:
+∞
𝑝 𝑥 𝑑𝑥 = 1
Does it make sense to talk about
the probability of someone being
exactly 2m?
𝒑 ?𝒉 𝒅𝒉
P(X=h)
𝒑 𝟏. 𝟒 ≤ 𝒉 < 𝟏. 𝟔 =
Clearly not, but we could for
example find the probability
of a height being in a
particular range.
−∞
1.0m 1.2m 1.4m 1.6m 1.8m 2.0m 2.2m 2.4m 2.6m
Height of randomly picked person (h)
The probability associated with a particular value is known as the probability density. It’s
value alone is not particular meaningful (and can be greater than 1!), but finding the area in
a range gives us a probability mass. This is similar to histograms, where the y-axis is the
‘frequency density’, and finding the area under the bars gives us the frequency.
Probability Density
Question: Archers fire arrows at a target. The probability of the arrow being a
certain distance from the centre of the target is proportional to this distance. No
archer is terrible enough that his arrow will be more than 1m from the centre.
What’s the probability that an arrow is less than 0.5m from the centre?
Source: Frosty Special
Answer: 𝑝 𝑥 ≤ 0.5𝑚 = 0.25
?
2
Probability is proportional to distance.
P(X=x)
Maximum distance is 1m.
Since area under graph must be 1,
then maximum probability density
must be 2, so that the area of
triangle is ½ x 2 x 1 = 1.
0.5
1
Distance of arrow from centre (x)
We’re finding the probability of the
arrow being between 0m and 0.5m,
so find the area under the graph in
this region. We can see this will be
0.25.
Probability Density
Question: Archers fire arrows at a target. The probability of the arrow being a
certain distance from the centre of the target is proportional to this distance. No
archer is terrible enough that his arrow will be more than 1m from the centre.
What’s the probability that an arrow is less than 0.5m from the centre?
Source: Frosty Special
Alternatively, using a cleaner integration approach:
Step 1: Use the information to express the proportionality relationship:
?
𝑝 𝑥 ∝𝑥
, so 𝑝 𝑥 = 𝑘𝑥.
Step 2: Determine constant by using the fact that
1
𝑘
?0
𝑏
𝑝
𝑎
𝑘
𝑘𝑥 𝑑𝑥 =
2
So 2 = 1 and thus 𝑘 = 2
Step 3: Finally, integrate desired range.
0.5
0
? 2𝑥 𝑑𝑥 = 0.25
𝑥 𝑑𝑥 = 1
Mean and Expected Value
Mean of a Sample
Mean of a Random Variable
But what about the mean of a random
variable X?
This is known as the “expected value of
X”, written E[X]. It can be calculated
using:
The process of using a random
variable to give us some values is
known as sampling. For example, we
might have measured the heights of a
sample of people:
𝐸𝑋 =
or 𝐸 𝑋 =
The mean of a sample you’ve known
how to do since primary school:
𝑥=
𝑥𝑖
𝑛
Archery scores: 57, 94, 25, 42
57 + 94 + 25 + 42
? = 54.5
𝑥=
4
𝑥 𝑝(𝑥)
𝑝 𝑥 𝑑𝑥
∞
𝑥
−∞
depending on whether your variable is
discrete or continuous.
X is “times target hit out of 3 shots”.
x
0
1
2
3
P(X=x)
0.25
0.5
0.05
0.2
𝐸𝑋
? + 2 × 0.05
= 0 × 0.25 + 1 × 0.5
Expected Value
Question: Two people randomly think of a real number between 0 and 100. What
is the expected difference between their numbers? (i.e. the average range)
(Source: Frosty Special)
(Hint: Make your random variable the difference between the two numbers )
As with many problems, it’s easier to consider a simpler scenario.
Consider just say integers between 0 and 10. How many ways can the numbers
be chosen if the range is 0? Or the range is 1? Or 2? What do you notice?
Step 1: Use the information to express the proportionality relationship:
𝑝 𝑥 ∝ 100 − 𝑥
We can consider the two numbers (with range 𝑥), as a ‘window’ which we can
‘slide’ in the 0 to 100 region. The bigger?the window, the less we can slide it. If
they were to choose 0 and 100, we can’t slide at all.
Step 2: Determine constant by using the fact that
𝑏
𝑝
𝑎
𝑥 𝑑𝑥 = 1
𝑝 𝑥 = 100𝑘 − 𝑘𝑥
𝑘
Integrating we get 100𝑘𝑥 − 2 𝑥 2 . If the?limits are 0 and 100, we get 5000𝑘 =
1
1, so 𝑘 = 5000
Expected Value
Question: Two people randomly think of a real number between 0 and 100. What
is the expected difference between their numbers? (i.e. the average range)
(Source: Frosty Special)
(Hint: Make your random variable the difference between the two numbers )
Step 3: Finally, given our known PDF, find E[X]
100
𝐸𝑋 =
𝑥 𝑝 𝑥 𝑑𝑥
100
0
𝑥 𝑘?
100 − 𝑥
=
𝑑𝑥
0
1
= 33
3
One of the harder problem sheet exercises is to consider what happens
when we introduce a 3rd number!
Modifying Random Variables
We often modify the value of random variables.
Example: X = outcome of a single throw of a die,
Y = outcome of another die
Consider X + 1
What does it mean?
We add 1 to all the outcomes of the die (i.e. we
now have 2 to 7)
?
The probabilities remain unaffected.
How does the expected
value change?
Clearly the mean value will also increase by one.
i.e.: 𝐸 𝑋 + 1 = 𝐸 𝑋 +?1
In general: 𝐸 𝑎𝑋 + 𝑏 = 𝑎𝐸 𝑋? + 𝑏
Modifying Random Variables
We often modify the value of random variables.
Example: X = outcome of a single throw of a die,
Y = outcome of another die
Now consider X + Y
What does it mean?
We consider all possible outcomes of X and Y,
and combine them by adding them. The new
? Clearly we need to
set of outcomes is 2 to 12.
recalculate the probabilities.
Uniform Distribution
A uniform distribution is one where all outcomes are equally likely.
Discrete Example
Continuous Example
You throw a fair die. What’s the probability
of each outcome?
1
𝑝 𝑎𝑛𝑦 𝑜𝑢𝑡𝑐𝑜𝑚𝑒 =
6
(This ensures the probabilities add up to 1).
?
You’re generating a random triangle. You
𝜋
pick an angle in the range 0 < 𝜃 < 2 to
use to construct your triangle, chosen
from a uniform distribution.
What is the probability (density) of
picking a particular angle?
2
,
𝑝 𝜃 = 𝜋
𝜋
𝑖𝑓 0 < 𝜃 <
2
0
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
?
This ensures the area under your PDF
𝜋
graph (a rectangle with width 2 and
2
height 𝜋) is 1.
Standard Deviation and Variance
Standard Deviation gives a measure of ‘spread’. It can roughly be thought of as the
average distance of values from the mean. It’s often represented by the letter 𝜎.
The variance is the standard deviation squared. i.e. 𝜎 2
Variance of a Sample
We find the average of the squares of
the displacements from the mean.
Example:
1cm 4cm 7cm 12cm
Mean = 6
Displacements are -5, -2, 1, 6
So variance is:
−5 2 + −2 2 + 12 + 62
= 16.5
4
Variance of a Random Variable
This is very similar to the sample variation.
We’re finding the average of the squared
displacement from the mean, i.e.:
𝑉𝑎𝑟[𝑋] = 𝐸[ 𝑋 − 𝜇 2 ]
Using the fact that 𝐸 𝑎𝑋 + 𝑏 = 𝑎𝐸 𝑋 + 𝑏:
𝐸 𝑋 − 𝜇 2 = 𝐸 𝑋 2 − 2𝜇𝑋 + 𝜇2
= 𝐸 𝑋 2 − 𝐸 2𝜇𝑋 + 𝐸 𝜇2 The expected value of a value
is just the value itself.
= 𝐸 𝑋 2 − 2𝜇𝐸 𝑋 + 𝜇2
Since 𝜇 =
= 𝐸 𝑋 2 − 2𝐸 𝑋 2 + 𝐸 𝑋 2
𝐸[𝑋]
= 𝐸 𝑋2 − 𝐸 𝑋 2
i.e. We can find the “mean of the squares minus
the square of the mean”.
Standard Deviation and Variance
Example: Find the variance of this biased spinner (which just has the values 1 and
2), represented by the random variable X.
k
1
2
P(X=k)
0.6
0.4
𝐸 𝑋 = 0.6 × 1 + 0.4 × 2 = 1.4
𝐸 𝑋 2 = 0.6 × 12 + 0.4 × 22 = 2.2
?
So 𝑉𝑎𝑟 𝑋 = 𝐸 𝑋 2 − 𝐸 𝑋
2
= 2.2 − 1.42 = 0.24
STEP Question
Fire extinguishers may become faulty at any time after manufacture and are tested
annually on the anniversary of manufacture. The time T years after manufacture until
a fire extinguisher becomes faulty is modelled by the continuous probability density
function:
𝑓 𝑡 =
2𝑡
1 + 𝑡2
0,
2,
𝑓𝑜𝑟 𝑡 ≥ 0
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
A faulty fire extinguisher will fail an annual test with probability p, in which case it is
destroyed immediately. A non-faulty fire extinguisher will always pass the test. All of
the annual tests are independent.
a) Show that the probability that a randomly chosen fire extinguisher will be
destroyed exactly three years after its manufacture is 𝑝(5𝑝2 − 13𝑝 + 9)/10
(We’ll do part (b) a bit later)
b) Find the probability that a randomly chosen fire extinguisher that was destroyed
exactly three years after its manufacture was faulty 18 months after its manufacture.
What might be going
through your head:
“I need to consider
each of the 3 cases.”
“I have a PDF. This requires me to
use definite integration.”
STEP Question
𝑓 𝑡 =
2𝑡
1 + 𝑡2
0,
2
,
𝑓𝑜𝑟 𝑡 ≥ 0
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Since we have a PDF, it makes sense to integrate it so we can find the probability of the extinguisher failing between
some range of times.
2𝑡
1 + 𝑡2
2
𝑑𝑡 = −
1
+𝑐
1 + 𝑡2
The probability the extinguisher fails sometime in the first year is
and during the third year
3
2
−
1
1+𝑡 2
=
1
0
−
1
1+𝑡 2
1
= , during the second year
2
2
1
−
1
1+𝑡 2
=
3
10
1
10
Let’s consider the three cases:
a)
If it fails during the first year, it must survive the first two tests, before failing the third. This gives a probability of
𝟏
𝟏 − 𝒑 𝟐𝒑
𝟐
b)
c)
𝟑
If it fails during the second year, it must survive the second test and fail on the third, giving
𝟏 − 𝒑 𝒑 (note
𝟏𝟎
that on the first test, the probability of it surviving given it’s not faulty is 1)
𝟏
If it fails during the third year, then it fails during the third year. We get 𝒑.
Adding these probabilities together gives us the desired probability.
𝟏𝟎
Mean and Variance of Random Variables
A point P is chosen (with uniform distribution) on the circle 𝑥 2 + 𝑦 2 = 1. The
random variable 𝑋 denotes the distance of 𝑃 from (1,0). Find the mean and
variance of X. [Source: STEP1 1987]
An important first question is how we could chosen a random point on the
circle with uniform distribution.
Question: Could we for example choose the x coordinate randomly between
-1 and 1, and use 𝑥 2 + 𝑦 2 = 1 to determine 𝑦?
Click to choose
points uniformly
across x.
No: We can see that because the lines are steeper
either side of the circle, we’d likely have less points in
these regions, and thus we haven’t chosen a point
with uniform distribution around the circle. We’d
have a similar problem if we were trying to pick a
random point on a sphere, and picked a random
latitude/longitude coordinate (we’d favour the poles)
Mean and Variance of Random Variables
A point P is chosen (with uniform distribution) on the circle 𝑥 2 + 𝑦 2 = 1. The
random variable 𝑋 denotes the distance of 𝑃 from (1,0). Find the mean and
variance of X. [Source: STEP1 1987]
In which case, how can we make sure we pick a point randomly?
Introduce a parameter 𝜃 for the angle anticlockwise
from the x-axis say. Clearly this doesn’t give bias to
?
certain regions of the arc (satisfying
the ‘uniform
distribution’ bit).
𝑋
So what is the distance X?
𝜃
2 sin
2
?
(It’s an isosceles triangle, so split into 2)
𝜃
(1,0)
So what is E[X]?
2𝜋
𝑥𝑝 𝑥 =
0
𝜃 1
2sin
?
2 2𝜋
1
=
𝜋
2𝜋
0
𝜃
sin
2
𝟒
=
𝝅
Summary
• Random variables have a number of possible outcomes, each with an associated
probability.
• Random variables can be discrete or continuous.
• Discrete random variables have an associated probability mass function. We require
that 𝑓(𝑥) = 1 across the domain of the function (i.e. possible outcomes).
• Continuous random variables have an associated probability density function.
Unlike ‘conventional’ probabilities, these can have a value greater than 1. We
∞
require that −∞ 𝑓 𝑥 𝑑𝑥 = 1, i.e. the total area under the graph is 1.
We can find a probability mass (i.e. the ‘conventional’ kind of probability) by finding
the area under the graph in a particular range, using definite integration.
• While we have a ‘mean’ for a sample, we have an ‘expected value’ for a random variable,
written 𝐸[𝑋]. It can be calculated using 𝑥 𝑝(𝑥) for a discrete random variable, and
∞
𝑥 𝑝 𝑥 𝑑𝑥 for a continuous random variable. The expected value for a fair die for
−∞
example is 3.5.
• The variance gives a measure of spread. For specifically it’s the average squared distance
from the mean. We can calculate it using 𝑽𝒂𝒓 𝑿 = 𝑬 𝑿𝟐 − 𝑬 𝑿 𝟐 , which can be
remembered using the mnemonic “mean of the square minus the square of the mean”,
or “msmsm”.
• 𝐸 𝑎𝑋 + 𝑏 = 𝑎𝐸 𝑋 + 𝑏. i.e. Scaling our outcomes/adding has the same effect on the
mean.
Topic 6 – Probability
Part 3: Common Distributions
Common Distributions
We’ve seen so far that can build whatever random variable we like using two essential
ingredients: specifying the outcomes, and specifying a PMF/PDF that associates a
probability with each outcome. But there’s a number of well-known distributions for
which we already have the outcomes and probability function defined: we just need to
set some parameters.
Bernoulli
Multivariate
Binomial
e.g. Throw of a
(possible biased) coin.
e.g. Throw of a (possibly
biased) die.
e.g. Counts the number of
heads and tails in 10
throws.
Multinomial
Poisson
Geometric
e.g. Counting the
number of each face in
10 throws of a die.
e.g. Number of cars which
pass in the next hour given
a known average rate.
e.g. The number of times
I have to flip a coin before
I see a heads.
We won’t
explore these.
Exponential
Dirichlet
e.g. The possible time
before a volcano next
erupts.
e.g. The possible probability distributions for the throw
of a die, given I threw a die 60 times and saw 10 ones,
10 twos, 10 threes, 10 fours, 10 fives and 10 sixes.
Bernoulli Distribution
The Bernoulli Distribution is perhaps the most simple distribution. It
models an experiment with just two outcomes, often referred to as
‘success’ and ‘failure’.
It might represent the single throw of a coin. (where ‘Heads’ could
represent a ‘success’)
Description
A single trial with
two outcomes.
Outcomes
“Failure”/”Success”,
or {0, 1} ?
Parameters?
p, the
probability
? of
success.
Probability Function
1−𝑝 𝑥 =0
𝑃 𝑋 = 𝑥 =?
𝑝
𝑥=1
A trial with just two outcomes is known as a Bernoulli Trial.
A sequence of Bernoulli Trials (all independent of each other) is known as a
Bernoulli Process.
An example is repeatedly flipping a coin, and recording the result each time.
Binomial Distribution
Suppose I flip a biased coin. Let heads be a ‘success’ and
tails be a ‘failure’. Let there be a probability 𝑝 that I have a
success in each throw.
The Binomial Distribution allows us to determine the
probability of a given number of successes in n (Bernoulli)
trials, in this case, the number of heads in n throws.
Question: If I throw a biased coin (with probability of heads p) 8 times, what is the
probability I see 3 heads?
H
H
H
T
T
T
T
T
The probability of this particular sequence is: 𝑝3 1 −
?𝑝5
But there’s 83? ways in which we could see 3 heads in 8 throws.
Therefore 𝑝 3 𝐻𝑒𝑎𝑑𝑠 = 83 𝑝3 1? − 𝑝 5
Binomial Distribution
Therefore, in general, the probability of k successes in n trials is:
𝑝 𝑋=𝑘 =
𝑛
𝑘
𝑝𝑘 1 − 𝑝
𝑛−𝑘
Description
Outcomes
Parameters?
Probability Function
Binomial D
Number of
‘successes’ in n
trials.
{0, 1, 2, … , n}
i.e. between 0
?
and n successes.
𝑝, the probability
𝑛 𝑘
𝑝
𝑋
=
𝑘
=
𝑝 1−𝑝
of a single success.
𝑘
? of
?
𝑛, the number
trials
We can write B(n,p) to represent the Binomial Distribution, where n and p are
the parameters for the number of trials and probability of a single success.
If we want some random variable X to use this distribution, we can use
𝑿~𝑩(𝒏, 𝒑). The ~ means “has the distribution of”.
𝑛−𝑘
Frost Real-Life Example
While on holiday in Hawaii, I was having lunch with a family, where an unusually high
number were left-handed: 5 out of the 8 of us (including myself). I was asked what
the probability of this was. (Roughly 10% of the world population is left-handed.)
Suppose X is the random variable representing the
number of left handed people.
Then 𝑋~𝐵 8, 0.1
?
8
× 0.15 × 0.93
5
?
= 1 𝑖𝑛 2450 𝑐ℎ𝑎𝑛𝑐𝑒
𝑃 𝑋=5 =
(This example points out one of the assumptions of the Binomial Distribution: that each trial
is independent. But this was unlikely to be the case, since most on the table were related,
and left-handedness is in part hereditary. Sometimes when we model a scenario using an
‘off-the-shelf’ distribution, we have to compromise by making simplifying assumptions.)
Summary of Distributions so far
Similarly, a multivariate distribution represents a single trial with any number of
outcomes.
A multinomial distribution is a generalisation of the Binomial Distribution, which
gives us the probability of counts when we have multiple outcomes.
Generalise to
n trials
Bernoulli
e.g. “What’s the
probability of getting a
Heads?”
Binomial
e.g. “What’s the
probability of getting 3
Heads and 2 Tails?”
Generalise to
k outcomes
Multivariate
e.g. “What’s the
probability of getting
a 5?
Generalise to
n trials
Multinomial
e.g. “What’s the
probability of rolling
3 sixes, 2 fours and a
1?
(Use your combinatorics knowledge to try and work out the probability function for this!)
Poisson Distribution
Cars pass you on a road at an average rate of 5
cars a minute. What’s the probability that 3
cars will pass you in the next minute?
When you have a known average ‘rate’ of
events occurring, we can use a Poisson
Distribution to model the number of events that
occur within that period.
We use 𝜆 to represent the average rate.
We can see that when the average rate is
10 (say per minute), we’re most likely to
see 10 cars. But technically, we could see
a million cars (even if the probability is
very low!)
k is the number of events (e.g. seeing a
car) that occur.
Poisson Distribution
Assumptions that the Poisson Distribution makes:
1. All events occur independently (e.g. a car passing
you doesn’t affect when the next car will pass you).
2. Events occur equally likely at any of time (e.g. we’re
not any more likely to see cars at the beginning of
the period than at the end)
Description
Outcomes
Number of events
occurring within a
fixed period given
an average rate.
{0, 1, 2, … } up to
infinity.
?
i.e. The Poisson
Distribution is a DISCRETE
distribution.
Parameters?
𝜆, the average
number of
events ?
in that
period.
Probability Function
𝜆𝑘 −𝜆
𝑃 𝑋=𝑘 = 𝑒
? 𝑘!
𝑒 is Euler’s Number, with the
value 2.71…
Poisson Distribution
Example: An active volcano erupts on average 5 times each year. It’s
equally likely to erupt at any time.
Q1) What’s the probability that it erupts 10 times next year?
510 −5
𝑝 𝑋 = 10? =
𝑒
10!
= 0.018
Q2) What’s the probability that it erupts at all next year?
1−𝑝 𝑋 =0
50 −5
= 1 −? 𝑒
0!
= 1 − 𝑒 −5
= 0.993
Q3) What’s the probability that it next erupts between 2 and 3 years
after the current date?
i.e. It erupts 0 times in the first year, 0 times in the second year, and at
least once the third year.
?
−5
−5
−5
𝑒 ×𝑒 × 1−𝑒
= 𝑒 −10 − 𝑒 −15
Relationship to the Binomial Distribution
Imagine that we segment this fixed period into a number of smaller chunks of time, in
each of which an event can occur (which we’ll describe as a ‘success’), or not occur.
1 minute
A car passed in
this period!
A car passed in
this period!
If we presumed that we only had at most one car passing in each of these smaller
periods of time, then we could use a Binomial Distribution to model the total number
of cars that pass across 1 minute, because it models the number of successes.
Of course, multiple cars could actually pass within each smaller segment of time.
How would we fix this?
Relationship to the Binomial Distribution
We could simply use smaller chunks of time – in the limit, we have tiny slivers of time, so
instantaneous that we couldn’t possibly have two cars passing at exactly the same time.
1 minute
Now if we’d divided up our time into 𝑛 chunks where 𝑛 is large, and we
expect an average of 𝜆 cars to pass, what then is the probability 𝑝 of a
car passing in one chunk of time? (Only Year 8 probability needed!)
𝝀
𝒑= ?
𝒏
Therefore, as n becomes infinitely large (so our slivers of time become
instantaneous moments), we can use the Binomial Distribution to represent the
number of events that occur within some period:
𝑝 𝑋=𝑘
𝑛
= lim
𝑛→∞ 𝑘
𝜆
𝑛
𝑘
𝜆
1−
𝑛
𝑛−𝑘
𝜆𝑘 −𝜆
= 𝑒
𝑘!
We need some fiddly
maths to show this. 𝑒
tends to arise in
maths when we have
limits.
Uniform Distribution
We saw earlier that a uniform distribution is where each outcome is equally likely.
Description
Each outcome is
equally likely.
Outcomes
x1, x2, …, xn
Parameters?
None.
?
Examples: The throw of a fair die, the
throw of a fair coin, the possible lottery
numbers this week (presuming the ball
machine isn’t biased!).
?
Probability Function
𝑝 𝑋 = 𝑥𝑖 =
?
1
𝑛
∀𝑖
Geometric Distribution
You, Christopher Walken, are captured by the Viet Cong during the Vietnam War, and
forced to play Russian Roulette. The gun has 6 slots on the barrel, one of which has a
bullet, and the other slots empty. Before each shot, you rotate the barrel randomly,
then shoot at your own head. If you survive, you repeat this ordeal.
Q1) What’s the probability that you die on the first shot?
Q2) What’s the probability that you die on the second shot?
𝟓
𝟔
You survive the first then die?on the second: ×
𝟏
𝟔
=
𝟓
𝟑𝟔
Q3) What’s the probability that you die on the 𝑥 𝑡ℎ shot?
p x =
𝟓 𝒙−𝟏
𝟔
?
×
𝟏
𝟔
𝟏
?
𝟔
Geometric Distribution
If you have a number of trials, where in each trial you can have a ‘success’ or ‘failure’,
and you repeat the trial until you have a success (at which point you stop), then the
geometric distribution gives you the probability of succeeding on the 1st trial, the 2nd
trial, and so on.
Description
Succeeding on the
xth trial after
previously failing.
Outcomes
Parameters?
{ 1, 2, 3, … }
The trial on which
?
you succeed.
The probability
𝑝 of success.
?
Probability Function
𝑝 𝑥 = 1−𝑝
𝑥−1
𝑝
?
𝟏
Note that if 𝑋~𝐺𝑒𝑜𝑚(𝑝), then 𝑬 𝑿 = 𝒑
For example, if we tossed a fair die until we saw a 1, we’d expect to have to throw the die
1
1 ÷ 6 = 6 times on average before we see a 1 (where the count includes the last throw).
Side Note: The distribution is called ‘geometric’ because if we were to list out the
probabilities for 𝑝(1), 𝑝(2), 𝑝(3) and so on, we’d have a geometric series!
Geometric Distribution
Tom and Geri have a competition. Initially, each player has one attempt at hitting a target. If
one player hits the target and the other does not then the successful player wins. If both
players hit the target, or if both players miss the target, then each has another attempt,
4
with the same rules applying. If the probability of Tom hitting the target is always 5 and the
2
probability of Geri hitting the target is always 3, what is the probability that Tom wins the
competition?
4
A: 
15
4
5
D: 
B:
8

15
E:
13

15
2
3
C: 
4
2
1
1
3
The probability that they both hit or miss is 5 × 3 + 5 × 3 = 5.
4
1
4
So Tom can win by either winning immediately 5 × 3 = 15, or initially
3
4
drawing before winning: 5 × 15, or drawing twice and then winning:
3 2
4
×
and so on. This gives us an infinite geometric series with
5
5
4
3
𝑎
2
𝑎 = 15 and 𝑟 = 5. Using 1−𝑟, we get 3.
SMC
Level 5
Level 4
Level 3
Level 2
Level 1
Frost Real-Life Example
My mum (who works at John Lewis), was selling London Olympics ‘trading cards’, of
which there were about 200 different cards to collect, and could be bought in packs.
Her manager was curious how many cards you would have to buy on average before
you collected them all. The problem was passed on to me!
(Note: Assume for simplicity that each card is equally likely to be acquired – unlike say ‘Pokemon cards’ [a
childhood fad I never got into], where lower numbered cards are rarer)
Hint: Perhaps think of the trials needed to collect the next card as a geometric process?
Then consider these processes all combined together.
Answer: 𝟏𝟏𝟕𝟔?cards
Explanation on next slide…
Frost Real-Life Example
Answer: 𝟏𝟏𝟕𝟔 cards
To get the first card, we just need to buy 1 card.
199
To get the second card, each time we buy a card, we have a 200 chance of buying a new
card (if not, we keep buying until we have a new one). Since the number of cards we need
to buy to get this next card is geometrically distributed, we expected number of cards is
1
200
=
.
𝑝
199
Combined these expected number of cards we need to buy for each new card, we get
200
200
200
200
+
+
+
⋯
+
200
199
198
1
𝟏
𝟏
𝟏
= 𝟐𝟎𝟎 𝟏 + 𝟐 + 𝟑 + ⋯ + 𝟐𝟎𝟎 . The bracketed expression is
known as a ‘Harmonic Series’, which can be represented as 𝑯𝟐𝟎𝟎 . Typing “200 * H(200)”
into www.wolframalpha.com got me the answer above.
This problem is more generally known as the “Coupon Collector’s Problem”
http://en.wikipedia.org/wiki/Coupon_collector%27s_problem
Coin Conundrums
How would you model a fair coin given you have just a fair die?
Solution: Easy! Roll the die. If you get say an even number, declare
?
‘Heads’, else declare ‘Tails’.
How would you model a fair die given you have just a fair coin?
Solution: A bit harder! Throw the coin 3 times, giving us 8 possible outcomes. Label the
first 6 of these outcomes (e.g. HHH, HHT, …). If we get the last two outcomes, then reject
these outcomes and repeat.
An interesting side question is how many times on average we’d expect to have to throw
?
6
3
the coin. If the probability of being able to stop is p = 8 = 4, the expected value of a
1
4
geometric distribution is 𝑝, i.e. 3. Since we throw the coin 3 times each time, then we
expect an average of 4 throws.
How would you model a fair coin using an unfair coin?
Solution: Suppose the probability of Heads on the unfair coin is 𝑝. Then throw this
coin two times. We have four outcomes: HH, HT, TH and TT, with probabilities 𝑝2 ,
𝑝(1 − 𝑝), 𝑝(1 − 𝑝) and 1 − 𝑝 2 respectively.
? Two of these outcomes have the same
probability. So declare ‘Heads’ if you threw HT on the biased coin, ‘Tails’ if you threw
HT, and repeat otherwise. (You’d expect to have to throw 𝑝(𝑝 − 1) times on average).
Gaussian/Normal Distribution
A Gaussian/Normal distribution is a continuous distribution which has a ‘bell-curve’
type shape. It’s useful for modelling variables where the values are clustered,
about the mean, and spread out around it with probability dropping off.
P(X=x)
IQ is a good example. The mean is (by
definition) 100, and the probability of
having an IQ drops off symmetrically, with
Standard Deviation 15 (by definition).
70
85
100
115 130 145
IQ (“Intelligence Quotient”) x
Suppose there’s a known mean 𝜇 and a
known standard deviation 𝜎. We
distribution is denoted as 𝑁(𝜇, 𝜎 2 ),
parameterised by the mean and
variance.
Then if 𝑋~𝑁 𝜇, 𝜎 2 :
𝑝 𝑋=𝑥 =
1
𝜎 2𝜋
𝑒
−
𝑥−𝜇 2
2𝜎2
Z-values
We might be interested to know what
percentage of the population have an IQ
below 130.
The z-value is the number of Standard
Deviations above the mean.
P(X=x)
Some rather helpful mathematicians have
compiled a table of values that give us
𝑃 𝑥 < 𝑧 , i.e. the probability of being below a
particular z-value, for different values of z. This
is unsurprisingly known as a z-table.
70
85
100
115 130 145
IQ (“Intelligence Quotient”) x
If 𝜎 = 15 and 𝜇 = 100, how many
Standard Deviations above the mean
is 130?
Answer = 2 ?
Z-values
…and our hundredths
digit here.
We look up the units
and tenths digit of
our z-value here…
So 𝑃 𝑥 ≤ 130 = 0.9772
?
Z-values
It’s useful to remember that 68% of values are within 1 s.d. of the mean, 95%
within two, and 99.7% within 3 (when the variable is ‘normally distributed’)
When scientists referred to a “5𝜎 test” needed to officially ‘discover’ the Higgs Boson,
they mean that were the data observed to occur ‘by chance’ in the situation where the
Higgs Boson didn’t exist (known as the null hypothesis), then the probability is less than
that of being 5𝜎 away from the mean of a randomly distributed variable: a 1 in 3.5m
chance. A Level students studying S2 will encounter ‘hypothesis testing’.